Managing stimulation of regional innovation subjects’ interaction in the digital economy

The reported study was funded by RFBR according to the research project No. 18-01000204_a, No. 16-07-00031_a, No. 18-07-00975_a.Purpose: The article is devoted to solving fundamental scientific problems in the scope of the development of forecasting modeling methods and evaluation of regional company’s innovative development parameters, synthesizing new methods of big data processing and intelligent analysis, as well as methods of knowledge eliciting and forecasting the dynamics of regional innovation developments through benchmarking. Design/Methodology/Approach: For regional economic development, it is required to identify the mechanisms that contribute to (or impede) the innovative economic development of the regions. The synergetic approach to management is based on the fact that there are multiple paths of IS development (scenarios with different probabilities), although it is necessary to reach the required attractor by meeting the management goals. Findings: The present research is focused on obtainment of new knowledge in creating a technique of multi-agent search, collection and processing of data on company’s innovative development indicators, models and methods of intelligent analysis of the collected data. Practical Implications: The author developed recommendations before starting the process of institutional changes in a specific regional innovation system. The article formulates recommendations on the implementation of institutional changes in the region taking into account the sociocultural characteristics of the region’s population. Originality/Value: It is the first time, when a complex of models and methods is based on the use of a convergent model of large data volumes processing is presented.peer-reviewe

Whose Tweets are Surveilled for the Police: An Audit of Social-Media Monitoring Tool via Log Files

Social media monitoring by law enforcement is becoming commonplace, but little is known about what software packages for it do. Through public records requests, we obtained log files from the Corvallis (Oregon) Police Department's use of social media monitoring software called DigitalStakeout. These log files include the results of proprietary searches by DigitalStakeout that were running over a period of 13 months and include 7240 social media posts. In this paper, we focus on the Tweets logged in this data and consider the racial and ethnic identity (through manual coding) of the users that are therein flagged by DigitalStakeout. We observe differences in the demographics of the users whose Tweets are flagged by DigitalStakeout compared to the demographics of the Twitter users in the region, however, our sample size is too small to determine significance. Further, the demographics of the Twitter users in the region do not seem to reflect that of the residents of the region, with an apparent higher representation of Black and Hispanic people. We also reconstruct the keywords related to a Narcotics report set up by DigitalStakeout for the Corvallis Police Department and find that these keywords flag Tweets unrelated to narcotics or flag Tweets related to marijuana, a drug that is legal for recreational use in Oregon. Almost all of the keywords have a common meaning unrelated to narcotics (e.g.\ broken, snow, hop, high) that call into question the utility that such a keyword based search could have to law enforcement.Comment: 21 Pages, 2 figures. To to be Published in FAT* 2020 Proceeding

A Coherent Unsupervised Model for Toponym Resolution

Toponym Resolution, the task of assigning a location mention in a document to a geographic referent (i.e., latitude/longitude), plays a pivotal role in analyzing location-aware content. However, the ambiguities of natural language and a huge number of possible interpretations for toponyms constitute insurmountable hurdles for this task. In this paper, we study the problem of toponym resolution with no additional information other than a gazetteer and no training data. We demonstrate that a dearth of large enough annotated data makes supervised methods less capable of generalizing. Our proposed method estimates the geographic scope of documents and leverages the connections between nearby place names as evidence to resolve toponyms. We explore the interactions between multiple interpretations of mentions and the relationships between different toponyms in a document to build a model that finds the most coherent resolution. Our model is evaluated on three news corpora, two from the literature and one collected and annotated by us; then, we compare our methods to the state-of-the-art unsupervised and supervised techniques. We also examine three commercial products including Reuters OpenCalais, Yahoo! YQL Placemaker, and Google Cloud Natural Language API. The evaluation shows that our method outperforms the unsupervised technique as well as Reuters OpenCalais and Google Cloud Natural Language API on all three corpora; also, our method shows a performance close to that of the state-of-the-art supervised method and outperforms it when the test data has 40% or more toponyms that are not seen in the training data.Comment: 9 pages (+1 page reference), WWW '18 Proceedings of the 2018 World Wide Web Conferenc

Modelling driving behaviour and its impact on the energy management problem in hybrid electric vehicles

Perfect knowledge of future driving conditions can be rarely assumed on real applications when optimally splitting power demands among different energy sources in a hybrid electric vehicle. Since performance of a control strategy in terms of fuel economy and pollutant emissions is strongly affected by vehicle power requirements, accurate predictions of future driving conditions are needed. This paper proposes different methods to model driving patterns with a stochastic approach. All the addressed methods are based on the statistical analysis of previous driving patterns to predict future driving conditions, some of them employing standard vehicle sensors, while others require non-conventional sensors (for instance, global positioning system or inertial reference system). The different modelling techniques to estimate future driving conditions are evaluated with real driving data and optimal control methods, trading off model complexity with performance.Guardiola García, C.; Plá Moreno, B.; Blanco Rodriguez, D.; Reig Bernad, A. (2014). Modelling driving behaviour and its impact on the energy management problem in hybrid electric vehicles. International Journal of Computer Mathematics. 91(1):147-156. doi:10.1080/00207160.2013.829567S147156911Ericsson, E. (2001). Independent driving pattern factors and their influence on fuel-use and exhaust emission factors. Transportation Research Part D: Transport and Environment, 6(5), 325-345. doi:10.1016/s1361-9209(01)00003-7Q. Gong, P. Tulpule, V. Marano, S. Midlam-Mohler, and G. Rizzoni,The role of ITS in PHEV performance improvement, 2011 American Control Conference, June–July, San Francisco, CA, 2011, pp. 2119–2124.C. Guardiola, B. Pla, S. Onori, and G. Rizzoni,A new approach to optimally tune the control strategy for hybrid vehicles applications, IFAC Workshop on Engine and Powertrain Control, Simulation and Modeling E-COSM’12, October, Rueil-Malmaison, France, 2012.Johannesson, L., Asbogard, M., & Egardt, B. (2007). Assessing the Potential of Predictive Control for Hybrid Vehicle Powertrains Using Stochastic Dynamic Programming. IEEE Transactions on Intelligent Transportation Systems, 8(1), 71-83. doi:10.1109/tits.2006.884887Liu, S., & Yao, B. (2008). Coordinate Control of Energy Saving Programmable Valves. IEEE Transactions on Control Systems Technology, 16(1), 34-45. doi:10.1109/tcst.2007.903073Paganelli, G. (2001). General supervisory control policy for the energy optimization of charge-sustaining hybrid electric vehicles. JSAE Review, 22(4), 511-518. doi:10.1016/s0389-4304(01)00138-2Rizzoni, G., Guzzella, L., & Baumann, B. M. (1999). Unified modeling of hybrid electric vehicle drivetrains. IEEE/ASME Transactions on Mechatronics, 4(3), 246-257. doi:10.1109/3516.789683Control of hybrid electric vehicles. (2007). IEEE Control Systems, 27(2), 60-70. doi:10.1109/mcs.2007.338280L. Serrao, S. Onori, and G. Rizzoni,ECMS as realization of Pontryagin's minimum principle for HEV control, 2009 American Control Conference, June, Saint Louis, MO, 2009, pp. 3964–3969.Serrao, L., Onori, S., & Rizzoni, G. (2011). A Comparative Analysis of Energy Management Strategies for Hybrid Electric Vehicles. Journal of Dynamic Systems, Measurement, and Control, 133(3). doi:10.1115/1.4003267Stockar, S., Marano, V., Canova, M., Rizzoni, G., & Guzzella, L. (2011). Energy-Optimal Control of Plug-in Hybrid Electric Vehicles for Real-World Driving Cycles. IEEE Transactions on Vehicular Technology, 60(7), 2949-2962. doi:10.1109/tvt.2011.2158565Sundström, O., Ambühl, D., & Guzzella, L. (2009). On Implementation of Dynamic Programming for Optimal Control Problems with Final State Constraints. Oil & Gas Science and Technology – Revue de l’Institut Français du Pétrole, 65(1), 91-102. doi:10.2516/ogst/2009020O. Sundström and L. Guzzella,A generic dynamic programming Matlab function, 18th IEEE International Conference on Control Applications Part of 2009 IEEE Multi-conference on Systems and Control, July, Saint Petersburg, 2009, pp. 1625–1630.R. Wang and S.M. Lukic,Review of driving conditions prediction and driving style recognition based control algorithms for hybrid electric vehicles, Vehicle Power and Propulsion Conference (VPPC), 2011 IEEE, September 6–9, Raleigh, NC, 2011, pp. 1–7

Estimating mobility of tourists. New Twitter-based procedure

Twitter has been actively researched as a human mobility proxy. Tweets can contain two classes of geographical metadata: the location from which a tweet was published, and the place where the tweet is estimated to have been published. Nevertheless, Twitter also presents tweets without any geographical metadata when querying for tweets on a specific location. This study presents a methodology which includes an algorithm for estimating the geographical coordinates to tweets for which Twitter doesn't assign any. Our objective is to determine the origin and the route that a tourist followed, even if Twitter doesn't return geographically identified data. This is carried out through geographical searches of tweets inside a defined area. Once a tweet is found inside an area, but its metadata contains no explicit geographical coordinates, its coordinates are estimated by iteratively performing geographical searches, with a decreasing geographical searching radius. This algorithm was tested in two touristic villages of Madrid (Spain) and a major city in Canada. A set of tweets without geographical coordinates in these areas were found and processed. The coordinates of a subset of them were successfully estimated.Agencia Estatal de Investigación | Ref. PID2020-116040RB-I00Universidade de Vigo/CISU

Multifaceted Geotagging for Streaming News

News sources on the Web generate constant streams of information, describing the events that shape our world. In particular, geography plays a key role in the news, and understanding the geographic information present in news allows for its useful spatial browsing and retrieval. This process of understanding is called geotagging, and involves first finding in the document all textual references to geographic locations, known as toponyms, and second, assigning the correct lat/long values to each toponym, steps which are termed toponym recognition and toponym resolution, respectively. These steps are difficult due to ambiguities in natural language: some toponyms share names with non-location entities, and further, a given toponym can have many location interpretations. Removing these ambiguities is crucial for successful geotagging. To this end, geotagging methods are described which were developed for streaming news. First, a spatio-textual search engine named STEWARD, and an interactive map-based news browsing system named NewsStand are described, which feature geotaggers as central components, and served as motivating systems and experimental testbeds for developing geotagging methods. Next, a geotagging methodology is presented that follows a multifaceted approach involving a variety of techniques. First, a multifaceted toponym recognition process is described that uses both rule-based and machine learning–based methods to ensure high toponym recall. Next, various forms of toponym resolution evidence are explored. One such type of evidence is lists of toponyms, termed comma groups, whose toponyms share a common thread in their geographic properties that enables correct resolution. In addition to explicit evidence, authors take advantage of the implicit geographic knowledge of their audiences. Understanding the local places known by an audience, termed its local lexicon, affords great performance gains when geotagging articles from local newspapers, which account for the vast majority of news on the Web. Finally, considering windows of text of varying size around each toponym, termed adaptive context, allows for a tradeoff between geotagging execution speed and toponym resolution accuracy. Extensive experimental evaluations of all the above methods, using existing and two newly-created, large corpora of streaming news, show great performance gains over several competing prominent geotagging methods

Twitter as an Indicator of Sports Activities in the Helsinki Metropolitan Area

Fyysinen aktiivisuus vaikuttaa vahvasti yksilön terveyteen ja hyvinvointiin. Alueellisen eriytymisen ehkäisyn ja ympäristöllisen tasa-arvon kannalta on tärkeää, että eri alueiden asukkailla on yhtäläiset mahdollisuudet harrastaa liikuntaa. Avoimesti saatavilla olevia kattavia tutkimuksia ihmisten fyysisestä aktiivisuudesta eri puolilla pääkaupunkiseutua ei juurikaan ole tehty, paikallisia liikuntabarometrejä lukuun ottamatta. Virallisten ja kattavien tietolähteiden puutteessa käyttäjien itse tuottamaa dataa, kuten sosiaalisen median dataa, voidaan mahdollisesti käyttää fyysisen aktiivisuuden arviointiin. Tässä tutkielmassa pyrin vastaamaan kysymyksiin: 1) kuinka Twitter-dataa voidaan käyttää indikaattorina liikunnallisen aktiivisuuden arviointiin, 2) miten liikunta-aiheistet twiitit ovat jakautuneet pääkaupunkiseudulla ja 3) mitkä sosio-ekonomiset tekijät selittävät twiittien lukumäärää alueella. Liikunta-aiheisten twiittien keräämiseen hyödynsin hakua urheiluun ja liikuntaan liittyvien avainsanalistojen avulla. Haetut avainsanat sisälsivät suomen-, englannin- ja vironkielisiä termejä. Tutkimuksen alueellisen luonteen takia tarvitsin geotägättyjä twiittejä, joihin on liitetty tieto paikan koordinaateista. Vain alle 1 % twiiteistä sisältää geotägin, joten hyödynsin geoparsing-tekniikkaa tuottaakseni lisää paikkaan sidottua aineistoa. Geoparsing tarkoittaa paikan nimien tunnistamista tekstistä ja niiden muuttamista koordinaateiksi. Yhdistin geotägätyt ja geoparsing-tekniikalla sijoitetut twiitit ja ryhmitin datan postinumeroalueittain. Postinumeroalueittain ryhmitetystä datasta tein spatiaalisia ja tilastollisia analyysejä mitatakseni spatiaalista autokorrelaatiota sekä korrelaatiota eri sosio-ekonomisten muuttujien kanssa. Tulokseni osoittavat, että urheilu- ja liikunta-aiheiset twiitit keskittyvät pääasiassa Helsingin keskustaan, mihin myös väestö on keskittynyt. Helsingin keskustan lisäksi on nähtävissä paikallisempia klustereita Tapiolassa, Leppävaarassa, Tikkurilassa ja Pasilassa. Twiittien urheilulajittainen tarkastelu paljastaa mailapeli- ja hiihtotwiittien keskittyneen voimakkaasti vastaavien urheilupaikkojen ympärille. Tilastoanalyysit osoittavat, että postinumeroalueen tuloilla ja koulutustasolla ei ole yhteyttä alueella havaittuun urheilutwiittien määrään. Parhaiten urheilutwiittien määrää ennustaa liikuntapaikkojen määrä, työllisyystaso ja lasten (0–14-vuotiaat) osuus väestöstä. Avaimia onnistuneeseen vastaavaan Twitter-tutkimukseen ovat geoparsing, riittävä datan määrä ja tarpeeksi hyvä kielimalli. Tämän tutkimuksen lupaavista tuloksista huolimatta Twitteriä fyysisen aktiivisuuden indikaattorina tulee tutkia lisää kartoittamalla tarkemmin sosiaalisen median sisäsyntyisiä vinoumia ennen kuin Twitter-tutkimusten tuloksia voidaan soveltaa oikean elämän ratkaisuihin.Being physically active is one of the key aspects of health. Thus, equal opportunities for exercising in different places is one important factor of environmental justice and segregation prevention. Currently, there are no openly available scientific studies about actual physical activities in different parts of the Helsinki Metropolitan Area other than sports barometers. In the lack of comprehensive official data sources, user-generated data, like social media, may be used as a proxy for measuring the levels and geographical distribution of sports activities. In this thesis, I aim to assess 1) how Twitter tweets could be used as an indicator of sports activities, 2) how the sports tweets are distributed spatially and 3) which socio-economic factors can predict the number of sports tweets. For recognizing the tweets related to sports, out of 38.5 million tweets, I used Named Entity Matching with a list of sports-related keywords in Finnish, English and Estonian. Due to the spatial nature of my study, I needed tweets that contain a geotag, meaning that the tweet is attached to coordinates that indicate a location. However, only about 1% of tweets contain a geotag, and since 2019 Twitter doesn’t support precise geotagging anymore with some exceptions. Therefore, I implemented geoparsing methods to search for location names in the text and transform them to coordinates if the mentioned place was within the study area. After that, I aggregated the posts to postal code areas and used statistical and spatial methods to measure spatial autocorrelation and correlation with different socio-economic variables to examine the spatial patterns and socio-economic factors that affect the tweeting about sports. My results show that the sports tweets are concentrated mainly in the center of Helsinki, where the population is also concentrated. The distribution of the sports tweets exhibits local clusters like Tapiola, Leppävaara, Tikkurila and Pasila besides the largest cluster in the center of Helsinki. Sports-wise mapping of the tweets reveals that for example racket sport and skiing tweets are heavily concentrated around the corresponding facilities. Statistical analyses indicate that the number of tweets per inhabitant does not correlate with the education level or the amount of average income in the postal code area. The factors that predict the number of tweets per inhabitant are number of sports facilities per inhabitant, employment, and percentage of children (0-14 years old) in the postal code area. Keys to a successful study when analyzing Twitter data are geoparsing, having enough data, and a good language model to process it. Despite the promising results of this study, Twitter as indicator of physical activity should be studied more to better understand the kind of bias it inherently has before basing real-life decisions on Twitter research

Introduction to the second international symposium of platial information science

People ‘live’ and constitute places every day through recurrent practices and experience. Our everyday lives, however, are complex, and so are places. In contrast to abstract space, the way people experience places includes a range of aspects like physical setting, meaning, and emotional attachment. This inherent complexity requires researchers to investigate the concept of place from a variety of viewpoints. The formal representation of place – a major goal in GIScience related to place – is no exception and can only be successfully addressed if we consider geographical, psychological, anthropological, sociological, cognitive, and other perspectives. This year’s symposium brings together place-based researchers from different disciplines to discuss the current state of platial research. Therefore, this volume contains contributions from a range of fields including geography, psychology, cognitive science, linguistics, and cartography